Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10194
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 MiB
Average record size in memory913.4 B

Variable types

Numeric5
Text8
DateTime2
Categorical6

Alerts

Category is highly overall correlated with Sub-CategoryHigh correlation
Discount is highly overall correlated with ProfitHigh correlation
Profit is highly overall correlated with Discount and 1 other fieldsHigh correlation
Sales is highly overall correlated with ProfitHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Country/Region is highly imbalanced (86.1%) Imbalance
Row ID is uniformly distributed Uniform
Row ID has unique values Unique
Discount has 4925 (48.3%) zeros Zeros

Reproduction

Analysis started2025-05-18 11:11:27.754929
Analysis finished2025-05-18 11:11:32.908748
Duration5.15 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

Uniform  Unique 

Distinct10194
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5097.5
Minimum1
Maximum10194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2025-05-18T16:41:33.022131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile510.65
Q12549.25
median5097.5
Q37645.75
95-th percentile9684.35
Maximum10194
Range10193
Interquartile range (IQR)5096.5

Descriptive statistics

Standard deviation2942.8987
Coefficient of variation (CV)0.57732195
Kurtosis-1.2
Mean5097.5
Median Absolute Deviation (MAD)2548.5
Skewness0
Sum51963915
Variance8660652.5
MonotonicityStrictly increasing
2025-05-18T16:41:33.173431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6800 1
 
< 0.1%
6793 1
 
< 0.1%
6794 1
 
< 0.1%
6795 1
 
< 0.1%
6796 1
 
< 0.1%
6797 1
 
< 0.1%
6798 1
 
< 0.1%
6799 1
 
< 0.1%
6801 1
 
< 0.1%
Other values (10184) 10184
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10194 1
< 0.1%
10193 1
< 0.1%
10192 1
< 0.1%
10191 1
< 0.1%
10190 1
< 0.1%
10189 1
< 0.1%
10188 1
< 0.1%
10187 1
< 0.1%
10186 1
< 0.1%
10185 1
< 0.1%
Distinct5111
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Memory size627.3 KiB
2025-05-18T16:41:33.437819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters142716
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2593 ?
Unique (%)25.4%

Sample

1st rowUS-2021-103800
2nd rowUS-2021-112326
3rd rowUS-2021-112326
4th rowUS-2021-112326
5th rowUS-2021-141817
ValueCountFrequency (%)
us-2024-100111 14
 
0.1%
us-2024-157987 12
 
0.1%
us-2023-108504 11
 
0.1%
us-2023-165330 11
 
0.1%
us-2022-131338 10
 
0.1%
us-2023-105732 10
 
0.1%
us-2022-126977 10
 
0.1%
us-2022-164882 9
 
0.1%
us-2023-114013 9
 
0.1%
us-2022-163433 9
 
0.1%
Other values (5101) 10089
99.0%
2025-05-18T16:41:33.751854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 28046
19.7%
- 20388
14.3%
1 17879
12.5%
0 15757
11.0%
U 9994
 
7.0%
S 9994
 
7.0%
4 8865
 
6.2%
3 8205
 
5.7%
6 5429
 
3.8%
5 5376
 
3.8%
Other values (5) 12783
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 142716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 28046
19.7%
- 20388
14.3%
1 17879
12.5%
0 15757
11.0%
U 9994
 
7.0%
S 9994
 
7.0%
4 8865
 
6.2%
3 8205
 
5.7%
6 5429
 
3.8%
5 5376
 
3.8%
Other values (5) 12783
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 142716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 28046
19.7%
- 20388
14.3%
1 17879
12.5%
0 15757
11.0%
U 9994
 
7.0%
S 9994
 
7.0%
4 8865
 
6.2%
3 8205
 
5.7%
6 5429
 
3.8%
5 5376
 
3.8%
Other values (5) 12783
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 142716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 28046
19.7%
- 20388
14.3%
1 17879
12.5%
0 15757
11.0%
U 9994
 
7.0%
S 9994
 
7.0%
4 8865
 
6.2%
3 8205
 
5.7%
6 5429
 
3.8%
5 5376
 
3.8%
Other values (5) 12783
9.0%
Distinct1242
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size79.8 KiB
Minimum2021-01-03 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-18T16:41:33.879927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:34.051730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1338
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size79.8 KiB
Minimum2021-01-07 00:00:00
Maximum2025-01-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-18T16:41:34.206835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:34.368248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size615.7 KiB
Standard Class
6120 
Second Class
1979 
First Class
1548 
Same Day
 
547

Length

Max length14
Median length14
Mean length12.834216
Min length8

Characters and Unicode

Total characters130832
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowStandard Class
3rd rowStandard Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 6120
60.0%
Second Class 1979
 
19.4%
First Class 1548
 
15.2%
Same Day 547
 
5.4%

Length

2025-05-18T16:41:34.515827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T16:41:34.629067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
class 9647
47.3%
standard 6120
30.0%
second 1979
 
9.7%
first 1548
 
7.6%
same 547
 
2.7%
day 547
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 22981
17.6%
s 20842
15.9%
d 14219
10.9%
10194
7.8%
l 9647
7.4%
C 9647
7.4%
S 8646
 
6.6%
n 8099
 
6.2%
r 7668
 
5.9%
t 7668
 
5.9%
Other values (8) 11221
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 22981
17.6%
s 20842
15.9%
d 14219
10.9%
10194
7.8%
l 9647
7.4%
C 9647
7.4%
S 8646
 
6.6%
n 8099
 
6.2%
r 7668
 
5.9%
t 7668
 
5.9%
Other values (8) 11221
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 22981
17.6%
s 20842
15.9%
d 14219
10.9%
10194
7.8%
l 9647
7.4%
C 9647
7.4%
S 8646
 
6.6%
n 8099
 
6.2%
r 7668
 
5.9%
t 7668
 
5.9%
Other values (8) 11221
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 22981
17.6%
s 20842
15.9%
d 14219
10.9%
10194
7.8%
l 9647
7.4%
C 9647
7.4%
S 8646
 
6.6%
n 8099
 
6.2%
r 7668
 
5.9%
t 7668
 
5.9%
Other values (8) 11221
8.6%
Distinct804
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size567.6 KiB
2025-05-18T16:41:35.099736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters81552
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowDP-13000
2nd rowPO-19195
3rd rowPO-19195
4th rowPO-19195
5th rowMB-18085
ValueCountFrequency (%)
wb-21850 41
 
0.4%
bf-11170 37
 
0.4%
gg-14650 36
 
0.4%
jw-15220 34
 
0.3%
jl-15835 34
 
0.3%
pp-18955 34
 
0.3%
ma-17560 34
 
0.3%
xp-21865 34
 
0.3%
eh-13765 32
 
0.3%
ck-12205 32
 
0.3%
Other values (794) 9846
96.6%
2025-05-18T16:41:35.483787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12177
14.9%
- 10194
12.5%
0 8666
 
10.6%
5 8049
 
9.9%
2 4786
 
5.9%
7 2963
 
3.6%
9 2960
 
3.6%
6 2955
 
3.6%
8 2860
 
3.5%
3 2839
 
3.5%
Other values (30) 23103
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12177
14.9%
- 10194
12.5%
0 8666
 
10.6%
5 8049
 
9.9%
2 4786
 
5.9%
7 2963
 
3.6%
9 2960
 
3.6%
6 2955
 
3.6%
8 2860
 
3.5%
3 2839
 
3.5%
Other values (30) 23103
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12177
14.9%
- 10194
12.5%
0 8666
 
10.6%
5 8049
 
9.9%
2 4786
 
5.9%
7 2963
 
3.6%
9 2960
 
3.6%
6 2955
 
3.6%
8 2860
 
3.5%
3 2839
 
3.5%
Other values (30) 23103
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12177
14.9%
- 10194
12.5%
0 8666
 
10.6%
5 8049
 
9.9%
2 4786
 
5.9%
7 2963
 
3.6%
9 2960
 
3.6%
6 2955
 
3.6%
8 2860
 
3.5%
3 2839
 
3.5%
Other values (30) 23103
28.3%
Distinct800
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size619.5 KiB
2025-05-18T16:41:35.736373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.940651
Min length7

Characters and Unicode

Total characters131917
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowDarren Powers
2nd rowPhillina Ober
3rd rowPhillina Ober
4th rowPhillina Ober
5th rowMick Brown
ValueCountFrequency (%)
michael 132
 
0.6%
frank 118
 
0.6%
john 107
 
0.5%
patrick 96
 
0.5%
brian 95
 
0.5%
stewart 93
 
0.5%
paul 92
 
0.4%
ken 91
 
0.4%
rick 91
 
0.4%
greg 90
 
0.4%
Other values (901) 19448
95.1%
2025-05-18T16:41:36.050658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12229
 
9.3%
e 12084
 
9.2%
n 10437
 
7.9%
10259
 
7.8%
r 9720
 
7.4%
i 8031
 
6.1%
l 6634
 
5.0%
o 5978
 
4.5%
t 5495
 
4.2%
s 4616
 
3.5%
Other values (47) 46434
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12229
 
9.3%
e 12084
 
9.2%
n 10437
 
7.9%
10259
 
7.8%
r 9720
 
7.4%
i 8031
 
6.1%
l 6634
 
5.0%
o 5978
 
4.5%
t 5495
 
4.2%
s 4616
 
3.5%
Other values (47) 46434
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12229
 
9.3%
e 12084
 
9.2%
n 10437
 
7.9%
10259
 
7.8%
r 9720
 
7.4%
i 8031
 
6.1%
l 6634
 
5.0%
o 5978
 
4.5%
t 5495
 
4.2%
s 4616
 
3.5%
Other values (47) 46434
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12229
 
9.3%
e 12084
 
9.2%
n 10437
 
7.9%
10259
 
7.8%
r 9720
 
7.4%
i 8031
 
6.1%
l 6634
 
5.0%
o 5978
 
4.5%
t 5495
 
4.2%
s 4616
 
3.5%
Other values (47) 46434
35.2%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size575.9 KiB
Consumer
5281 
Corporate
3090 
Home Office
1823 

Length

Max length11
Median length8
Mean length8.8396115
Min length8

Characters and Unicode

Total characters90111
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowHome Office
3rd rowHome Office
4th rowHome Office
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5281
51.8%
Corporate 3090
30.3%
Home Office 1823
 
17.9%

Length

2025-05-18T16:41:36.132564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T16:41:36.194493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5281
43.9%
corporate 3090
25.7%
home 1823
 
15.2%
office 1823
 
15.2%

Most occurring characters

ValueCountFrequency (%)
o 13284
14.7%
e 12017
13.3%
r 11461
12.7%
C 8371
9.3%
m 7104
7.9%
n 5281
 
5.9%
s 5281
 
5.9%
u 5281
 
5.9%
f 3646
 
4.0%
t 3090
 
3.4%
Other values (7) 15295
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 13284
14.7%
e 12017
13.3%
r 11461
12.7%
C 8371
9.3%
m 7104
7.9%
n 5281
 
5.9%
s 5281
 
5.9%
u 5281
 
5.9%
f 3646
 
4.0%
t 3090
 
3.4%
Other values (7) 15295
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 13284
14.7%
e 12017
13.3%
r 11461
12.7%
C 8371
9.3%
m 7104
7.9%
n 5281
 
5.9%
s 5281
 
5.9%
u 5281
 
5.9%
f 3646
 
4.0%
t 3090
 
3.4%
Other values (7) 15295
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 13284
14.7%
e 12017
13.3%
r 11461
12.7%
C 8371
9.3%
m 7104
7.9%
n 5281
 
5.9%
s 5281
 
5.9%
u 5281
 
5.9%
f 3646
 
4.0%
t 3090
 
3.4%
Other values (7) 15295
17.0%

Country/Region
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size616.0 KiB
United States
9994 
Canada
 
200

Length

Max length13
Median length13
Mean length12.862664
Min length6

Characters and Unicode

Total characters131122
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 9994
98.0%
Canada 200
 
2.0%

Length

2025-05-18T16:41:36.276230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T16:41:36.331378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united 9994
49.5%
states 9994
49.5%
canada 200
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 29982
22.9%
e 19988
15.2%
a 10594
 
8.1%
n 10194
 
7.8%
d 10194
 
7.8%
U 9994
 
7.6%
i 9994
 
7.6%
9994
 
7.6%
S 9994
 
7.6%
s 9994
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 131122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 29982
22.9%
e 19988
15.2%
a 10594
 
8.1%
n 10194
 
7.8%
d 10194
 
7.8%
U 9994
 
7.6%
i 9994
 
7.6%
9994
 
7.6%
S 9994
 
7.6%
s 9994
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 131122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 29982
22.9%
e 19988
15.2%
a 10594
 
8.1%
n 10194
 
7.8%
d 10194
 
7.8%
U 9994
 
7.6%
i 9994
 
7.6%
9994
 
7.6%
S 9994
 
7.6%
s 9994
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 131122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 29982
22.9%
e 19988
15.2%
a 10594
 
8.1%
n 10194
 
7.8%
d 10194
 
7.8%
U 9994
 
7.6%
i 9994
 
7.6%
9994
 
7.6%
S 9994
 
7.6%
s 9994
 
7.6%

City
Text

Distinct542
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size580.6 KiB
2025-05-18T16:41:36.535383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3083186
Min length4

Characters and Unicode

Total characters94889
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rowHouston
2nd rowNaperville
3rd rowNaperville
4th rowNaperville
5th rowPhiladelphia
ValueCountFrequency (%)
city 1010
 
7.0%
new 937
 
6.5%
york 920
 
6.4%
san 805
 
5.6%
los 747
 
5.2%
angeles 747
 
5.2%
philadelphia 537
 
3.7%
francisco 510
 
3.5%
seattle 428
 
3.0%
houston 377
 
2.6%
Other values (567) 7440
51.5%
2025-05-18T16:41:36.843745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8831
 
9.3%
o 7787
 
8.2%
a 7707
 
8.1%
n 6387
 
6.7%
i 6277
 
6.6%
l 6054
 
6.4%
s 4705
 
5.0%
r 4610
 
4.9%
t 4598
 
4.8%
4264
 
4.5%
Other values (43) 33669
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94889
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8831
 
9.3%
o 7787
 
8.2%
a 7707
 
8.1%
n 6387
 
6.7%
i 6277
 
6.6%
l 6054
 
6.4%
s 4705
 
5.0%
r 4610
 
4.9%
t 4598
 
4.8%
4264
 
4.5%
Other values (43) 33669
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94889
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8831
 
9.3%
o 7787
 
8.2%
a 7707
 
8.1%
n 6387
 
6.7%
i 6277
 
6.6%
l 6054
 
6.4%
s 4705
 
5.0%
r 4610
 
4.9%
t 4598
 
4.8%
4264
 
4.5%
Other values (43) 33669
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94889
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8831
 
9.3%
o 7787
 
8.2%
a 7707
 
8.1%
n 6387
 
6.7%
i 6277
 
6.6%
l 6054
 
6.4%
s 4705
 
5.0%
r 4610
 
4.9%
t 4598
 
4.8%
4264
 
4.5%
Other values (43) 33669
35.5%
Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size572.6 KiB
2025-05-18T16:41:36.985671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.505101
Min length4

Characters and Unicode

Total characters86701
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTexas
2nd rowIllinois
3rd rowIllinois
4th rowIllinois
5th rowPennsylvania
ValueCountFrequency (%)
california 2001
16.7%
new 1330
 
11.1%
york 1128
 
9.4%
texas 985
 
8.2%
pennsylvania 587
 
4.9%
washington 506
 
4.2%
illinois 492
 
4.1%
ohio 469
 
3.9%
florida 383
 
3.2%
carolina 291
 
2.4%
Other values (57) 3802
31.8%
2025-05-18T16:41:37.211324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10950
12.6%
i 10055
11.6%
n 8208
 
9.5%
o 7439
 
8.6%
r 5690
 
6.6%
e 5203
 
6.0%
l 4886
 
5.6%
s 4646
 
5.4%
C 2588
 
3.0%
f 2017
 
2.3%
Other values (39) 25019
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10950
12.6%
i 10055
11.6%
n 8208
 
9.5%
o 7439
 
8.6%
r 5690
 
6.6%
e 5203
 
6.0%
l 4886
 
5.6%
s 4646
 
5.4%
C 2588
 
3.0%
f 2017
 
2.3%
Other values (39) 25019
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10950
12.6%
i 10055
11.6%
n 8208
 
9.5%
o 7439
 
8.6%
r 5690
 
6.6%
e 5203
 
6.0%
l 4886
 
5.6%
s 4646
 
5.4%
C 2588
 
3.0%
f 2017
 
2.3%
Other values (39) 25019
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10950
12.6%
i 10055
11.6%
n 8208
 
9.5%
o 7439
 
8.6%
r 5690
 
6.6%
e 5203
 
6.0%
l 4886
 
5.6%
s 4646
 
5.4%
C 2588
 
3.0%
f 2017
 
2.3%
Other values (39) 25019
28.9%
Distinct654
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size537.3 KiB
2025-05-18T16:41:37.460108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9607612
Min length3

Characters and Unicode

Total characters50570
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)0.8%

Sample

1st row77095
2nd row60540
3rd row60540
4th row60540
5th row19143
ValueCountFrequency (%)
10035 263
 
2.6%
10024 230
 
2.3%
10009 229
 
2.2%
94122 203
 
2.0%
10011 193
 
1.9%
94110 166
 
1.6%
98105 165
 
1.6%
19134 160
 
1.6%
90049 151
 
1.5%
98103 151
 
1.5%
Other values (644) 8283
81.3%
2025-05-18T16:41:37.842782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10652
21.1%
1 7635
15.1%
2 5088
10.1%
9 4908
9.7%
4 4696
9.3%
3 4648
9.2%
7 3675
 
7.3%
5 3621
 
7.2%
8 2713
 
5.4%
6 2534
 
5.0%
Other values (15) 400
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10652
21.1%
1 7635
15.1%
2 5088
10.1%
9 4908
9.7%
4 4696
9.3%
3 4648
9.2%
7 3675
 
7.3%
5 3621
 
7.2%
8 2713
 
5.4%
6 2534
 
5.0%
Other values (15) 400
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10652
21.1%
1 7635
15.1%
2 5088
10.1%
9 4908
9.7%
4 4696
9.3%
3 4648
9.2%
7 3675
 
7.3%
5 3621
 
7.2%
8 2713
 
5.4%
6 2534
 
5.0%
Other values (15) 400
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10652
21.1%
1 7635
15.1%
2 5088
10.1%
9 4908
9.7%
4 4696
9.3%
3 4648
9.2%
7 3675
 
7.3%
5 3621
 
7.2%
8 2713
 
5.4%
6 2534
 
5.0%
Other values (15) 400
 
0.8%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size536.2 KiB
West
3253 
East
2986 
Central
2335 
South
1620 

Length

Max length7
Median length4
Mean length4.8460859
Min length4

Characters and Unicode

Total characters49401
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral
2nd rowCentral
3rd rowCentral
4th rowCentral
5th rowEast

Common Values

ValueCountFrequency (%)
West 3253
31.9%
East 2986
29.3%
Central 2335
22.9%
South 1620
15.9%

Length

2025-05-18T16:41:37.932897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T16:41:37.997928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west 3253
31.9%
east 2986
29.3%
central 2335
22.9%
south 1620
15.9%

Most occurring characters

ValueCountFrequency (%)
t 10194
20.6%
s 6239
12.6%
e 5588
11.3%
a 5321
10.8%
W 3253
 
6.6%
E 2986
 
6.0%
C 2335
 
4.7%
n 2335
 
4.7%
r 2335
 
4.7%
l 2335
 
4.7%
Other values (4) 6480
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 10194
20.6%
s 6239
12.6%
e 5588
11.3%
a 5321
10.8%
W 3253
 
6.6%
E 2986
 
6.0%
C 2335
 
4.7%
n 2335
 
4.7%
r 2335
 
4.7%
l 2335
 
4.7%
Other values (4) 6480
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 10194
20.6%
s 6239
12.6%
e 5588
11.3%
a 5321
10.8%
W 3253
 
6.6%
E 2986
 
6.0%
C 2335
 
4.7%
n 2335
 
4.7%
r 2335
 
4.7%
l 2335
 
4.7%
Other values (4) 6480
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 10194
20.6%
s 6239
12.6%
e 5588
11.3%
a 5321
10.8%
W 3253
 
6.6%
E 2986
 
6.0%
C 2335
 
4.7%
n 2335
 
4.7%
r 2335
 
4.7%
l 2335
 
4.7%
Other values (4) 6480
13.1%
Distinct1862
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size637.3 KiB
2025-05-18T16:41:38.143706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters152910
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.9%

Sample

1st rowOFF-PA-10000174
2nd rowOFF-BI-10004094
3rd rowOFF-LA-10003223
4th rowOFF-ST-10002743
5th rowOFF-AR-10003478
ValueCountFrequency (%)
fur-fu-10004270 20
 
0.2%
off-pa-10001970 19
 
0.2%
tec-ac-10003832 18
 
0.2%
fur-ch-10002647 15
 
0.1%
tec-ac-10002049 15
 
0.1%
fur-ch-10004287 15
 
0.1%
fur-ch-10001146 15
 
0.1%
tec-ac-10003628 15
 
0.1%
fur-ch-10003774 14
 
0.1%
fur-ta-10003473 14
 
0.1%
Other values (1852) 10034
98.4%
2025-05-18T16:41:38.368246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35765
23.4%
- 20388
13.3%
F 15695
10.3%
1 15294
10.0%
O 6430
 
4.2%
2 4969
 
3.2%
4 4931
 
3.2%
3 4892
 
3.2%
A 4494
 
2.9%
5 3455
 
2.3%
Other values (17) 36597
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35765
23.4%
- 20388
13.3%
F 15695
10.3%
1 15294
10.0%
O 6430
 
4.2%
2 4969
 
3.2%
4 4931
 
3.2%
3 4892
 
3.2%
A 4494
 
2.9%
5 3455
 
2.3%
Other values (17) 36597
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35765
23.4%
- 20388
13.3%
F 15695
10.3%
1 15294
10.0%
O 6430
 
4.2%
2 4969
 
3.2%
4 4931
 
3.2%
3 4892
 
3.2%
A 4494
 
2.9%
5 3455
 
2.3%
Other values (17) 36597
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35765
23.4%
- 20388
13.3%
F 15695
10.3%
1 15294
10.0%
O 6430
 
4.2%
2 4969
 
3.2%
4 4931
 
3.2%
3 4892
 
3.2%
A 4494
 
2.9%
5 3455
 
2.3%
Other values (17) 36597
23.9%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size615.3 KiB
Office Supplies
6128 
Furniture
2201 
Technology
1865 

Length

Max length15
Median length15
Mean length12.789778
Min length9

Characters and Unicode

Total characters130379
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffice Supplies
2nd rowOffice Supplies
3rd rowOffice Supplies
4th rowOffice Supplies
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 6128
60.1%
Furniture 2201
 
21.6%
Technology 1865
 
18.3%

Length

2025-05-18T16:41:38.450997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T16:41:38.514613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
office 6128
37.5%
supplies 6128
37.5%
furniture 2201
 
13.5%
technology 1865
 
11.4%

Most occurring characters

ValueCountFrequency (%)
e 16322
12.5%
i 14457
11.1%
p 12256
9.4%
f 12256
9.4%
u 10530
 
8.1%
c 7993
 
6.1%
l 7993
 
6.1%
O 6128
 
4.7%
s 6128
 
4.7%
S 6128
 
4.7%
Other values (10) 30188
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16322
12.5%
i 14457
11.1%
p 12256
9.4%
f 12256
9.4%
u 10530
 
8.1%
c 7993
 
6.1%
l 7993
 
6.1%
O 6128
 
4.7%
s 6128
 
4.7%
S 6128
 
4.7%
Other values (10) 30188
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16322
12.5%
i 14457
11.1%
p 12256
9.4%
f 12256
9.4%
u 10530
 
8.1%
c 7993
 
6.1%
l 7993
 
6.1%
O 6128
 
4.7%
s 6128
 
4.7%
S 6128
 
4.7%
Other values (10) 30188
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16322
12.5%
i 14457
11.1%
p 12256
9.4%
f 12256
9.4%
u 10530
 
8.1%
c 7993
 
6.1%
l 7993
 
6.1%
O 6128
 
4.7%
s 6128
 
4.7%
S 6128
 
4.7%
Other values (10) 30188
23.2%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size559.6 KiB
Binders
1548 
Paper
1384 
Furnishings
1009 
Phones
903 
Storage
856 
Other values (12)
4494 

Length

Max length11
Median length9
Mean length7.1979596
Min length3

Characters and Unicode

Total characters73376
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaper
2nd rowBinders
3rd rowLabels
4th rowStorage
5th rowArt

Common Values

ValueCountFrequency (%)
Binders 1548
15.2%
Paper 1384
13.6%
Furnishings 1009
9.9%
Phones 903
8.9%
Storage 856
8.4%
Art 821
8.1%
Accessories 775
7.6%
Chairs 634
6.2%
Appliances 474
 
4.6%
Labels 368
 
3.6%
Other values (7) 1422
13.9%

Length

2025-05-18T16:41:38.601228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1548
15.2%
paper 1384
13.6%
furnishings 1009
9.9%
phones 903
8.9%
storage 856
8.4%
art 821
8.1%
accessories 775
7.6%
chairs 634
6.2%
appliances 474
 
4.6%
labels 368
 
3.6%
Other values (7) 1422
13.9%

Most occurring characters

ValueCountFrequency (%)
s 10153
13.8%
e 8990
12.3%
r 7326
 
10.0%
i 5828
 
7.9%
n 5545
 
7.6%
a 4620
 
6.3%
o 3324
 
4.5%
p 3042
 
4.1%
h 2663
 
3.6%
c 2373
 
3.2%
Other values (18) 19512
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 10153
13.8%
e 8990
12.3%
r 7326
 
10.0%
i 5828
 
7.9%
n 5545
 
7.6%
a 4620
 
6.3%
o 3324
 
4.5%
p 3042
 
4.1%
h 2663
 
3.6%
c 2373
 
3.2%
Other values (18) 19512
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 10153
13.8%
e 8990
12.3%
r 7326
 
10.0%
i 5828
 
7.9%
n 5545
 
7.6%
a 4620
 
6.3%
o 3324
 
4.5%
p 3042
 
4.1%
h 2663
 
3.6%
c 2373
 
3.2%
Other values (18) 19512
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 10153
13.8%
e 8990
12.3%
r 7326
 
10.0%
i 5828
 
7.9%
n 5545
 
7.6%
a 4620
 
6.3%
o 3324
 
4.5%
p 3042
 
4.1%
h 2663
 
3.6%
c 2373
 
3.2%
Other values (18) 19512
26.6%
Distinct1849
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size876.7 KiB
2025-05-18T16:41:38.790289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length127
Median length78
Mean length36.962527
Min length5

Characters and Unicode

Total characters376796
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.9%

Sample

1st rowMessage Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book
2nd rowGBC Standard Plastic Binding Systems Combs
3rd rowAvery 508
4th rowSAFCO Boltless Steel Shelving
5th rowAvery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack
ValueCountFrequency (%)
xerox 871
 
1.5%
x 718
 
1.3%
615
 
1.1%
with 604
 
1.1%
avery 569
 
1.0%
for 551
 
1.0%
binders 528
 
0.9%
chair 493
 
0.9%
black 438
 
0.8%
phone 380
 
0.7%
Other values (2798) 51443
89.9%
2025-05-18T16:41:39.101588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46630
 
12.4%
e 34256
 
9.1%
r 21202
 
5.6%
o 20317
 
5.4%
a 19467
 
5.2%
i 19017
 
5.0%
l 16749
 
4.4%
n 15942
 
4.2%
s 14999
 
4.0%
t 14849
 
3.9%
Other values (75) 153368
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 376796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
46630
 
12.4%
e 34256
 
9.1%
r 21202
 
5.6%
o 20317
 
5.4%
a 19467
 
5.2%
i 19017
 
5.0%
l 16749
 
4.4%
n 15942
 
4.2%
s 14999
 
4.0%
t 14849
 
3.9%
Other values (75) 153368
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 376796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
46630
 
12.4%
e 34256
 
9.1%
r 21202
 
5.6%
o 20317
 
5.4%
a 19467
 
5.2%
i 19017
 
5.0%
l 16749
 
4.4%
n 15942
 
4.2%
s 14999
 
4.0%
t 14849
 
3.9%
Other values (75) 153368
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 376796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
46630
 
12.4%
e 34256
 
9.1%
r 21202
 
5.6%
o 20317
 
5.4%
a 19467
 
5.2%
i 19017
 
5.0%
l 16749
 
4.4%
n 15942
 
4.2%
s 14999
 
4.0%
t 14849
 
3.9%
Other values (75) 153368
40.7%

Sales
Real number (ℝ)

High correlation 

Distinct6161
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.22585
Minimum0.444
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2025-05-18T16:41:39.193893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.444
5-th percentile4.98
Q117.22
median53.91
Q3209.5
95-th percentile943.5351
Maximum22638.48
Range22638.036
Interquartile range (IQR)192.28

Descriptive statistics

Standard deviation619.90684
Coefficient of variation (CV)2.716199
Kurtosis306.36627
Mean228.22585
Median Absolute Deviation (MAD)44.964
Skewness12.983926
Sum2326534.4
Variance384284.49
MonotonicityNot monotonic
2025-05-18T16:41:39.299213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 56
 
0.5%
19.44 43
 
0.4%
15.552 39
 
0.4%
10.368 36
 
0.4%
32.4 28
 
0.3%
6.48 27
 
0.3%
25.92 24
 
0.2%
17.94 22
 
0.2%
20.736 19
 
0.2%
14.94 17
 
0.2%
Other values (6151) 9883
96.9%
ValueCountFrequency (%)
0.444 1
< 0.1%
0.556 1
< 0.1%
0.836 1
< 0.1%
0.852 1
< 0.1%
0.876 1
< 0.1%
0.898 1
< 0.1%
0.984 1
< 0.1%
0.99 1
< 0.1%
1.044 1
< 0.1%
1.08 1
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.968 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.976 1
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7918383
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2025-05-18T16:41:39.380515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2283168
Coefficient of variation (CV)0.58766135
Kurtosis1.9615258
Mean3.7918383
Median Absolute Deviation (MAD)1
Skewness1.2686325
Sum38654
Variance4.965396
MonotonicityNot monotonic
2025-05-18T16:41:39.457658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 2440
23.9%
3 2435
23.9%
5 1269
12.4%
4 1212
11.9%
1 933
 
9.2%
7 617
 
6.1%
6 585
 
5.7%
8 269
 
2.6%
9 259
 
2.5%
10 61
 
0.6%
Other values (4) 114
 
1.1%
ValueCountFrequency (%)
1 933
 
9.2%
2 2440
23.9%
3 2435
23.9%
4 1212
11.9%
5 1269
12.4%
6 585
 
5.7%
7 617
 
6.1%
8 269
 
2.6%
9 259
 
2.5%
10 61
 
0.6%
ValueCountFrequency (%)
14 30
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 61
 
0.6%
9 259
 
2.5%
8 269
 
2.6%
7 617
6.1%
6 585
5.7%
5 1269
12.4%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15538454
Minimum0
Maximum0.8
Zeros4925
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size79.8 KiB
2025-05-18T16:41:39.531310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.2
95-th percentile0.7
Maximum0.8
Range0.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.20624859
Coefficient of variation (CV)1.3273431
Kurtosis2.4162909
Mean0.15538454
Median Absolute Deviation (MAD)0.2
Skewness1.6874528
Sum1583.99
Variance0.042538482
MonotonicityNot monotonic
2025-05-18T16:41:39.608228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4925
48.3%
0.2 3706
36.4%
0.7 424
 
4.2%
0.8 301
 
3.0%
0.3 230
 
2.3%
0.4 207
 
2.0%
0.6 149
 
1.5%
0.1 96
 
0.9%
0.5 66
 
0.6%
0.15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4925
48.3%
0.1 96
 
0.9%
0.15 52
 
0.5%
0.2 3706
36.4%
0.3 230
 
2.3%
0.32 27
 
0.3%
0.4 207
 
2.0%
0.45 11
 
0.1%
0.5 66
 
0.6%
0.6 149
 
1.5%
ValueCountFrequency (%)
0.8 301
 
3.0%
0.7 424
 
4.2%
0.6 149
 
1.5%
0.5 66
 
0.6%
0.45 11
 
0.1%
0.4 207
 
2.0%
0.32 27
 
0.3%
0.3 230
 
2.3%
0.2 3706
36.4%
0.15 52
 
0.5%

Profit
Real number (ℝ)

High correlation 

Distinct7597
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.673417
Minimum-6599.978
Maximum8399.976
Zeros65
Zeros (%)0.6%
Negative1901
Negative (%)18.6%
Memory size79.8 KiB
2025-05-18T16:41:39.749416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-52.65883
Q11.7608
median8.69
Q329.297925
95-th percentile168.4496
Maximum8399.976
Range14999.954
Interquartile range (IQR)27.537125

Descriptive statistics

Standard deviation232.46512
Coefficient of variation (CV)8.1073391
Kurtosis401.69292
Mean28.673417
Median Absolute Deviation (MAD)10.7372
Skewness7.6049059
Sum292296.81
Variance54040.03
MonotonicityNot monotonic
2025-05-18T16:41:39.883251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.6%
6.2208 43
 
0.4%
9.3312 39
 
0.4%
3.6288 32
 
0.3%
5.4432 32
 
0.3%
15.552 26
 
0.3%
12.4416 21
 
0.2%
7.2576 19
 
0.2%
3.1104 18
 
0.2%
9.072 11
 
0.1%
Other values (7587) 9888
97.0%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-2929.4845 1
< 0.1%
-2639.9912 1
< 0.1%
-2287.782 1
< 0.1%
-1862.3124 1
< 0.1%
-1850.9464 1
< 0.1%
-1811.0784 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2799.984 1
< 0.1%
2591.9568 1
< 0.1%
2504.2216 1
< 0.1%

Interactions

2025-05-18T16:41:31.753143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.264024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.856729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.549275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.130861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.870750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.389655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.963415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.659284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.253658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.993603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.498742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.192620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.774906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.370153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:32.115857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.616910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.309310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.878439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.496007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:32.248477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:29.731884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:30.434671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.007205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T16:41:31.621723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T16:41:39.987247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CategoryCountry/RegionDiscountProfitQuantityRegionRow IDSalesSegmentShip ModeSub-Category
Category1.0000.0650.3720.0550.0000.0000.0000.0710.0000.0000.999
Country/Region0.0651.0000.0640.0000.0430.1280.0320.0000.0130.0490.093
Discount0.3720.0641.000-0.541-0.0010.290-0.001-0.0530.0000.0250.349
Profit0.0550.000-0.5411.0000.2350.020-0.0000.5200.0000.0060.128
Quantity0.0000.043-0.0010.2351.0000.000-0.0000.3260.0180.0000.002
Region0.0000.1280.2900.0200.0001.0000.0540.0000.0030.0180.000
Row ID0.0000.032-0.001-0.000-0.0000.0541.000-0.0030.0380.0350.000
Sales0.0710.000-0.0530.5200.3260.000-0.0031.0000.0070.0000.139
Segment0.0000.0130.0000.0000.0180.0030.0380.0071.0000.0300.000
Ship Mode0.0000.0490.0250.0060.0000.0180.0350.0000.0301.0000.012
Sub-Category0.9990.0930.3490.1280.0020.0000.0000.1390.0000.0121.000

Missing values

2025-05-18T16:41:32.470072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T16:41:32.705724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountry/RegionCityState/ProvincePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
01US-2021-1038002021-01-032021-01-07Standard ClassDP-13000Darren PowersConsumerUnited StatesHoustonTexas77095CentralOFF-PA-10000174Office SuppliesPaperMessage Book, Wirebound, Four 5 1/2" X 4" Forms/Pg., 200 Dupl. Sets/Book16.44820.25.5512
12US-2021-1123262021-01-042021-01-08Standard ClassPO-19195Phillina OberHome OfficeUnited StatesNapervilleIllinois60540CentralOFF-BI-10004094Office SuppliesBindersGBC Standard Plastic Binding Systems Combs3.54020.8-5.4870
23US-2021-1123262021-01-042021-01-08Standard ClassPO-19195Phillina OberHome OfficeUnited StatesNapervilleIllinois60540CentralOFF-LA-10003223Office SuppliesLabelsAvery 50811.78430.24.2717
34US-2021-1123262021-01-042021-01-08Standard ClassPO-19195Phillina OberHome OfficeUnited StatesNapervilleIllinois60540CentralOFF-ST-10002743Office SuppliesStorageSAFCO Boltless Steel Shelving272.73630.2-64.7748
45US-2021-1418172021-01-052021-01-12Standard ClassMB-18085Mick BrownConsumerUnited StatesPhiladelphiaPennsylvania19143EastOFF-AR-10003478Office SuppliesArtAvery Hi-Liter EverBold Pen Style Fluorescent Highlighters, 4/Pack19.53630.24.8840
56US-2021-1671992021-01-062021-01-10Standard ClassME-17320Maria EtezadiHome OfficeUnited StatesHendersonKentucky42420SouthFUR-CH-10004063FurnitureChairsGlobal Deluxe High-Back Manager's Chair2573.82090.0746.4078
67US-2021-1671992021-01-062021-01-10Standard ClassME-17320Maria EtezadiHome OfficeUnited StatesHendersonKentucky42420SouthOFF-AR-10001662Office SuppliesArtRogers Handheld Barrel Pencil Sharpener5.48020.01.4796
78US-2021-1060542021-01-062021-01-07First ClassJO-15145Jack O'BriantCorporateUnited StatesAthensGeorgia30605SouthOFF-AR-10002399Office SuppliesArtDixon Prang Watercolor Pencils, 10-Color Set with Brush12.78030.05.2398
89US-2021-1671992021-01-062021-01-10Standard ClassME-17320Maria EtezadiHome OfficeUnited StatesHendersonKentucky42420SouthOFF-BI-10004632Office SuppliesBindersIbico Hi-Tech Manual Binding System609.98020.0274.4910
910US-2021-1671992021-01-062021-01-10Standard ClassME-17320Maria EtezadiHome OfficeUnited StatesHendersonKentucky42420SouthOFF-FA-10001883Office SuppliesFastenersAlliance Super-Size Bands, Assorted Sizes31.12040.00.3112
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountry/RegionCityState/ProvincePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
1018410185CA-2024-1415312024-12-292025-01-02Standard ClassBG-18435Bruce GalangConsumerCanadaQuebec CityQuebecG1BEastFUR-FU-10004093FurnitureFurnishingsHand-Finished Solid Wood Document Frame68.46020.020.5380
1018510186CA-2024-1415312024-12-292025-01-02Standard ClassBG-18435Bruce GalangConsumerCanadaQuebec CityQuebecG1BEastOFF-FA-10000089Office SuppliesFastenersAcco Glide Clips19.60050.09.6040
1018610187US-2024-1432592024-12-302025-01-03Standard ClassPO-18865Patrick O'DonnellConsumerUnited StatesNew York CityNew York10009EastFUR-BO-10003441FurnitureBookcasesBush Westfield Collection Bookcases, Fully Assembled323.13640.212.1176
1018710188US-2024-1262212024-12-302025-01-05Standard ClassCC-12430Chuck ClarkHome OfficeUnited StatesColumbusIndiana47201CentralOFF-AP-10002457Office SuppliesAppliancesEureka The Boss Plus 12-Amp Hard Box Upright Vacuum, Red209.30020.056.5110
1018810189US-2024-1154272024-12-302025-01-03Standard ClassEB-13975Erica BernCorporateUnited StatesFairfieldCalifornia94533WestOFF-BI-10002103Office SuppliesBindersCardinal Slant-D Ring Binder, Heavy Gauge Vinyl13.90420.24.5188
1018910190US-2024-1432592024-12-302025-01-03Standard ClassPO-18865Patrick O'DonnellConsumerUnited StatesNew York CityNew York10009EastOFF-BI-10003684Office SuppliesBindersWilson Jones Legal Size Ring Binders52.77630.219.7910
1019010191US-2024-1154272024-12-302025-01-03Standard ClassEB-13975Erica BernCorporateUnited StatesFairfieldCalifornia94533WestOFF-BI-10004632Office SuppliesBindersGBC Binding covers20.72020.26.4750
1019110192US-2024-1567202024-12-302025-01-03Standard ClassJM-15580Jill MatthiasConsumerUnited StatesLovelandColorado80538WestOFF-FA-10003472Office SuppliesFastenersBagged Rubber Bands3.02430.2-0.6048
1019210193US-2024-1432592024-12-302025-01-03Standard ClassPO-18865Patrick O'DonnellConsumerUnited StatesNew York CityNew York10009EastTEC-PH-10004774TechnologyPhonesGear Head AU3700S Headset90.93070.02.7279
1019310194CA-2024-1435002024-12-302025-01-03Standard ClassHO-15230Harry OlsonConsumerCanadaCharlottetownPrince Edward IslandC0AEastOFF-BI-10004040Office SuppliesBindersWilson Jones Impact Binders3.02430.2-0.6048